code: def __init__(
      self,
      src_file_name: str,
      tgt_file_name: str,
      src_tokenizer: TokenizerSpec,
      tgt_tokenizer: TokenizerSpec,
      src_language: str,
      tgt_language: str,
      max_src_seq_length: int,
      max_tgt_seq_length: int,
      max_seq_length_dec: int,
      seed: int = 1234,
      max_num_samples: int = None,
      masked_lm_prob: float = 0.15,
      extreme_masked_lm_prob: float = 0.5,
      max_ngram_size: int = 10,
      mean_ngram_size: int = None,
      min_ngram_size: int = 1,
      extreme_max_ngram_size: int = 128,
      extreme_mean_ngram_size: int = 64,
      extreme_min_ngram_size: int = 32,
      extreme_ngram_span_length_distribution: LengthDistribution = LengthDistribution.truncated_normal,
      prefix_lm_pivot_mean: float = 0.25,  # This is represented as a percentage of the total length.
      geometric_dist: bool = True,
      permutation: bool = False,
      favor_long_ngrams: bool = False,
      sampling_ratios: Dict[str, float] = {
          "x-masking": 0.25,
          "r-masking": 0.25,
          "s-masking": 0.25,
          "nmt": 0.25,
      },
      sentinel_tokens: List[int] = None,
  ):
      super().__init__(
          src_file_name=src_file_name,
          tgt_file_name=tgt_file_name,
          src_tokenizer=src_tokenizer,
          tgt_tokenizer=tgt_tokenizer,
          max_src_seq_length=max_src_seq_length
          - 1,  # -1 here to account for <sep> tokens and special prefix tokens to the encoder like <extra_id_x>, <extra_id_r>, etc.
          max_tgt_seq_length=max_tgt_seq_length
          - 1,  # -1 here to account for <sep> tokens and special prefix tokens to the encoder like <extra_id_x>, <extra_id_r>, etc.
          seed=seed,
          max_num_samples=max_num_samples,
          add_bos_to_enc=False,
          add_eos_to_enc=False,
      )
      self.max_seq_length_dec = max_seq_length_dec
      self.max_ngram_size = max_ngram_size
      self.mean_ngram_size = mean_ngram_size
      self.min_ngram_size = min_ngram_size
      self.geometric_dist = geometric_dist
      self.permutation = permutation
      self.favor_long_ngrams = favor_long_ngrams
      self.extreme_masked_lm_prob = extreme_masked_lm_prob
      self.extreme_max_ngram_size = extreme_max_ngram_size
      self.extreme_mean_ngram_size = extreme_mean_ngram_size
      self.extreme_min_ngram_size = extreme_min_ngram_size
      self.extreme_ngram_span_length_distribution = (
          extreme_ngram_span_length_distribution
      )
      self.prefix_lm_pivot_mean = prefix_lm_pivot_mean
      self.sampling_ratios = sampling_ratios
      self.src_language = src_language
      self.tgt_language = tgt_language
  
      # Vocab stuff.
      self.vocab_id_list = src_tokenizer.vocab
      self.vocab_id_to_token_dict = {
          idx: token for idx, token in enumerate(self.vocab_id_list)
      }
      self.cls_id = src_tokenizer.cls_id
      self.sep_id = src_tokenizer.sep_id
      self.mask_id = src_tokenizer.mask_id
      self.pad_id = src_tokenizer.pad_id
      self.bos_id = src_tokenizer.bos_id
      self.eos_id = src_tokenizer.eos_id
      self.max_seq_length = max_src_seq_length + max_tgt_seq_length - 1
      self.masked_lm_prob = masked_lm_prob
      self.sentinel_tokens = sentinel_tokens
  
      self.tokenizer_type = T5Dataset._determine_tokenizer_type(
          src_tokenizer, whole_word_masking=False
      )
      self._build()
  
imports: from typing import Dict, List
  import numpy
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.common.sequence_to_sequence_dataset import BinarizedMemmapSequenceToSequenceDataset, TextMemmapSequenceToSequenceDataset
  from nemo.collections.nlp.data.language_modeling.megatron.bert_dataset import build_training_sample
  from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution
  from nemo.collections.nlp.data.language_modeling.megatron.t5_dataset import T5Dataset
  from nemo.collections.nlp.data.language_modeling.megatron.ul2_dataset import UL2Dataset
label_desc: Use image node_id 1 to create a new TextMemmapCrossLingualMLMAndTranslationDataset object from inherited base classes: TextMemmapSequenceToSequenceDataset with example: obj = TextMemmapCrossLingualMLMAndTranslationDataset(src_file_name, tgt_file_name, src_tokenizer, tgt_tokenizer, src_language, tgt_language, max_src_seq_length, max_tgt_seq_length, max_seq_length_dec, seed, max_num_samples, masked_lm_prob, extreme_masked_lm_prob, max_ngram_size, mean_ngram_size, min_ngram_size, extreme_max_ngram_size, extreme_mean_ngram_size, extreme_min_ngram_size, extreme_ngram_span_length_distribution, prefix_lm_pivot_mean, geometric_dist, permutation, favor_long_ngrams, sampling_ratios, sentinel_tokens)